Interactive Decision Maps

Decision-Making Strategies

In this video, you’ll learn strategies for making decisions large and small. Visit https://edu.gcfglobal.org/en/problem-solving-and-decision-making/ for our text-based tutorial. We hope you enjoy!

From playlist Making Decisions

Empathy Mapping | What Is An Empathy Map? | Empathy Mapping In Design Thinking | Simplilearn

In this tutorial we will learn what is empathy mapping, four-quadrants in Empathy mapping, theming and Difference between one user and multi-user empathy mapping. It is a visualization tool where designers gain an in-depth understanding of the user's behaviour and attitude. It also represe

From playlist Design Thinking [2022 Updated]

(ML 2.1) Classification trees (CART)

Basic intro to decision trees for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

Where 2012, Bruce Daniel, "Responsive Design--The Future of Mapping"

Responsive Design is all about how structure can adjust to various environments, user activities and form factors. First we'll look at the core concepts of Responsive Design and how they're applied in a typical web setting, noting the methods used. Then we'll see how maps naturally follow

From playlist Where 2012

How Decision Trees Work

Decision trees are powerful and surprisingly straightforward. Here's how they are grown. Code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py Slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing PERM

From playlist Data Science

Decision trees - A friendly introduction

A video about decision trees, and how to train them on a simple example. Accompanying blog post: https://medium.com/@luis.serrano/splitting-data-by-asking-questions-decision-trees-74afed9cd849 Helper videos: - Gini index: https://www.youtube.com/watch?v=u4IxOk2ijSs - Entropy and informat

From playlist Supervised Learning

Making Decisions

If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.

From playlist Making Decisions

Decision and Classification Trees, Clearly Explained!!!

Decision trees are part of the foundation for Machine Learning. Although they are quite simple, they are very flexible and pop up in a very wide variety of situations. This StatQuest covers all the basics and shows you how to create a new tree from scratch, one step at a time. NOTE: This

From playlist StatQuest

Understanding Decision tree in Business Analytics with R | Edureka

( R Training : https://www.edureka.co/r-for-analytics ) A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. The

From playlist R Tutorial Videos

Code-It-Yourself! Role Playing Game Part #3

Phew, almost done with this series now! In this video I introduce navigation, AI and quests to the role playing game. It's a bit code heavy this one, but I feel its an important step that shows how OOP (object oriented programming) can be exploited further to make developing reusable game

From playlist Code-It-Yourself!

5.1 Software Maps

From playlist Automated Visual Software Analytics by Prof. Dr. Jürgen Döllner

Marco Pavone: "On safe & efficient human-robot interactions via multimodal intent modeling & rea..."

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop II: Safe Operation of Connected and Autonomous Vehicle Fleets "On safe and efficient human-robot interactions via multimodal intent modeling and reachability-based safety assurance" Marco Pavone - Stanford Uni

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

Yuxin Chen: "Bayesian Experimental Design in the Physical Sciences"

Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Bayesian Experimental Design in the Physical Sciences" Yuxin Chen - University of Chicago Abstract: How can we intelligently acquire information for decision making,

From playlist Machine Learning for Physics and the Physics of Learning 2019

Olfactory Search and Navigation (Lecture 2) by Antonio Celani

PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR, I

From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)

RailsConf 2021: How to teach your code to describe its own architecture - Kevin Gilpin

Architecture documents and diagrams are always out of date. Unfortunately, lack of accurate, up-to-date information about software architecture is really harmful to developer productivity and happiness. Historically, developers have overcome other frustrations by making code the “source of

From playlist RailsConf 2021

O'Reilly Webcast: Best of Strata Rx 2012 - Doing Big Data All By Yourself

As healthcare organizations gain access to more and more data, they rely increasingly on complex systems and predictive tools to make sense of it all. This has led to a divide between those who perform data analysis (programmers, statisticians, database administrators) and those who make d

From playlist O'Reilly Webcasts 2

Lillian Ratliff - Learning via Conjectural Variations - IPAM at UCLA

Recorded 15 February 2022. Lillian Ratliff of the University of Washington presents "Learning via Conjectural Variations" at IPAM's Mathematics of Collective Intelligence Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/mathematics-of-intelligences/?tab=schedule

From playlist Workshop: Mathematics of Collective Intelligence - Feb. 15 - 19, 2022.

DSI | A Biased Tour of the Uncertainty Visualization Zoo | By Matthew Kay

Uncertain predictions permeate our daily lives (“will it rain today?”, “how long until my bus shows up?”, “who is most likely to win the next election?”). Fully understanding the uncertainty in such predictions would allow people to make better decisions, yet predictive systems usually com

From playlist DSI Virtual Seminar Series

Stanford Seminar - Driving Exploratory Visualization through Perception & Cognition

Danielle Szafir University of North Carolina - Chapel Hill November 5, 2021 Visualizations allow analysts to rapidly explore and make sense of their data. The ways we visualize data directly influence the conclusions we draw and decisions we make; however, our knowledge of how visualizati

From playlist Stanford Seminars

(ML 11.4) Choosing a decision rule - Bayesian and frequentist

Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.

From playlist Machine Learning